Twin support vector machines (TWSVMs) have been shown to be effective classifiers for a range of pattern classification tasks. However, the TWSVM formulation suffers from shortcomings: (i) uses hinge loss function which renders it sensitive dataset outliers (noise sensitivity). (ii) It requires matrix inversion calculation in Wolfe-dual is intractable datasets with large numbers features/sample...

This paper presents a novel decision-based fuzzy filter based on support vector machines and Dempster-Shafer evidence theory for effective noise suppression and detail preservation. The proposed filter uses an SVM impulse detector to judge whether an input pixel is noisy. Sources of evidence are extracted, and then the fusion of evidence based on the evidence theory provides a feature vector th...